MP-ALOE: an r2SCAN dataset for universal machine learning interatomic potentials

Journal Article · Wed Jan 01 00:00:00 EST 2025 · npj Computational Materials

DOI:https://doi.org/10.1038/s41524-025-01834-9· OSTI ID:3008754

Kuner, Matthew C; Kaplan, Aaron D; Persson, Kristin A; Asta, Mark; Chrzan, Daryl C

We present MP-ALOE, a dataset of nearly 1 million DFT calculations using the accurate r2SCAN meta-generalized gradient approximation. Covering 89 elements, MP-ALOE was created using active learning and primarily consists of off-equilibrium structures. We benchmark a machine learning interatomic potential trained on MP-ALOE, and evaluate its performance on a series of benchmarks, including predicting the thermochemical properties of equilibrium structures; predicting forces of far-from-equilibrium structures; maintaining physical soundness under static extreme deformations; and molecular dynamic stability under extreme temperatures and pressures. MP-ALOE shows strong performance on all of these benchmarks and is made public for the broader community to utilize.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: US Department of Energy; USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 3008754
• Journal Information: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 11
• Country of Publication: United States
• Language: English